In the field of digital image processing, the ability to robustly and precisely locate eye position in frontal facial images is of great importance for various biometric applications, including human-machine interfaces, remote control of equipment, and fatigue detection, among others. The search for the position of the iris within the face is a particularly crucial prior step for face recognition applications. In Campadelli P., Lanzarotti R., Lipori G., “Eye localization: A survey. The fundamentals of verbal and nonverbal communication and their biometrical issues”, NATO Sci. Series, Vol 18, 2007, pp. 234-245, it is demonstrated that a 5% error in center eye position (relative to the distance between the eyes) results in a 20% drop in the face recognition rate for different methods studied. This is why robust, precise, and computationally efficient methods for estimating center eye position are so important.
It has been demonstrated that different methods for locating eyes, and the iris in particular, are successful in specific applications. However, these methods are of limited use because they are invasive, or partially invasive, requiring helmet-mounted devices, use of electrodes to measure eyeball activity, use of near-infrared light to detect the pupil reflections, or cameras positioned very near to the eye.
In the case of non-invasive methods based on digital image processing, the majority of the techniques focus on progressively more complex detections or validations, whereby they first approximate eye position and then refine those estimates in a later step. Other models obtain eye position by detecting other facial features, such as the nose or mouth; this eliminates false detections by choosing those pairs of eye candidates, which meet certain statistical criteria related to their relative position with respect to other facial features.
Among the non-invasive methods, the main limitations are: the use of very high resolution images in the eye region, which requires highly controlled lighting and contrast conditions, or the requirement of open eyes for proper detection. Other methods requiring the detection of alternate facial characteristics, such as the nose and/or mouth and others produce high numbers of false positives within the face. The use of eyeglasses is problematic for those methods that are not robust to reflections, and the use of sunglasses prevents the proper functioning of the great majority of the methods, which are unable to estimate eye location.
Many of these methods assume that there has been prior facial detection, but they are not capable of discerning whether the input image is a validly detected face or not. Finally, some of these methods are computationally expensive, and thus cannot be used effectively in real-time applications. Therefore, a robust and efficient eye location method, which solves some of these formerly mentioned constraints, is still needed.
The solution proposed here is a non-invasive method of locating eyes within digital images, which delivers an accurate estimate of eye location, and also functions in non-optimal conditions, such as with eyes closed or with occlusions (e.g. sunglasses). Furthermore, this method does not require the detection of other facial features and is capable of validating whether or not the image is a frontal face. Additionally, its computational efficiency allows for real-time application.